adding init

app.py

@@ -0,0 +1,72 @@
+import torchaudio
+import gradio as gr
+from pyannote.audio import Pipeline
+from pyannote.audio.pipelines.utils.hook import ProgressHook
+import scipy.io.wavfile
+import os
+
+
+def perform_separation(audio_file_path: str):
+    # Instantiate the pipeline
+    pipeline = Pipeline.from_pretrained(
+        "pyannote/speech-separation-ami-1.0",
+        use_auth_token=HUGGINGFACE_ACCESS_TOKEN,
+    )
+
+    waveform, sample_rate = torchaudio.load(audio_file_path)
+
+    # Run the pipeline
+    with ProgressHook() as hook:
+        diarization, sources = pipeline(
+            {"waveform": waveform, "sample_rate": sample_rate}, hook=hook
+        )
+
+    # Save separated sources to disk as SPEAKER_XX.wav files
+    output_file_paths = []
+    for s, speaker in enumerate(diarization.labels()):
+        number_of_separated_sources = sources.data.shape[1]
+        if s >= number_of_separated_sources:
+            break
+
+        output_file_path = f"{speaker}.wav"
+        scipy.io.wavfile.write(
+            output_file_path, sample_rate, sources.data[:, s].numpy()
+        )
+        output_file_paths.append(output_file_path)
+
+    # Generate RTTM content
+    rttm_content = diarization.to_rttm()
+
+    return output_file_paths, rttm_content
+
+
+def gradio_wrapper(audio_file_path: str):
+    output_file_paths, rttm_content = perform_separation(audio_file_path)
+    return output_file_paths + [rttm_content]
+
+
+inputs = gr.inputs.Audio(label="Input Audio", type="filepath")
+
+# Dynamic output for audio files
+outputs = []
+max_speakers = 10  # Set a reasonable maximum number of speakers
+for i in range(max_speakers):
+    outputs.append(gr.outputs.Audio(label=f"Speaker {i+1}", type="filepath"))
+
+# Add RTTM output
+outputs.append(gr.outputs.Textbox(label="RTTM Output"))
+
+title = "Speech Separation and Diarization"
+description = "Gradio demo for Speech Separation and Diarization using Pyannote's pyannote/speech-separation-ami-1.0. To use it, simply upload your audio, or click one of the examples to load them. The app will output separated audio for each speaker and the RTTM file content."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2403.02288' target='_blank'>PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings</a> | <a href='https://huggingface.co/pyannote/speech-separation-ami-1.0' '_blank'>HuggingFace Pipeline</a></p>"
+examples = [["samples_audio_samples_test_mixture.wav"]]
+
+gr.Interface(
+    gradio_wrapper,
+    inputs,
+    outputs,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+).launch()

requirement.txt

@@ -0,0 +1,3 @@
+pyannote.audio[separation]==3.3.0
+torchaudio
+scipy